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          <h1 class="post-title" itemprop="name headline">ETF定投数据分析9——使用BT框架模拟交易</h1>
        

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        <p>距离上次文章已经过去几个月了，我一直在与模拟交易挣扎。代码已经能运行了，但是想添加止盈止损的策略，总是调不对。具体可以看项目github库(<a target="_blank" rel="noopener" href="https://github.com/zwdnet/etfdata)%E9%87%8C%E7%9A%84simulate%E5%88%86%E6%94%AF%E9%87%8C%E7%9A%84simulater2.py%E3%80%82">https://github.com/zwdnet/etfdata)里的simulate分支里的simulater2.py。</a><br>后来，我想不能再这么挣扎下去了。于是就在网上找量化投资的python库。几番尝试找到一个叫bt的库，是建立在ffn基础上的。试了一下，pydroid3里能装的，就用它吧。<br>官方文档地址: <a target="_blank" rel="noopener" href="http://pmorissette.github.io/bt/index.html">http://pmorissette.github.io/bt/index.html</a><br>以下翻译自官方文档:<br>bt是一个灵活的用于测试量化投资策略的Python回测框架。回测(Backtesting)是用给定的数据集测试量化投资策略的过程。本框架能使您容易的建立混合和匹配不同算法的策略。<br>目标是节省量化投资者重新造轮子的时间，使他们专注于工作的重要部分——策略开发。<br>一个例子</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> bt</span><br><span class="line"></span><br><span class="line"><span class="comment"># 首先下载数据</span></span><br><span class="line">data = bt.get(<span class="string">&#x27;spy,agg&#x27;</span>, start=<span class="string">&#x27;2010-01-01&#x27;</span>)</span><br><span class="line"><span class="string">&quot;&quot;&quot;这是通过yahoo下载的数据，要下载A股数据要用其它工具，例如tushare&quot;&quot;&quot;</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 接着，创建策略</span></span><br><span class="line">s = bt.Strategy(<span class="string">&#x27;s1&#x27;</span>, [bt.algos.RunMonthly(),</span><br><span class="line">                       bt.algos.SelectAll(),</span><br><span class="line">                       bt.algos.WeighEqually(),</span><br><span class="line">                       bt.algos.Rebalance()])</span><br><span class="line"><span class="string">&quot;&quot;&quot;第一个参数是策略名称，第二个策略是一个包含策略具体内容的list，包括操作间隔，选股策略，权重，和再平衡策略。这里都使用algos类里的函数，如果没满足需要也可以自定义。&quot;&quot;&quot;</span></span><br><span class="line"><span class="string">&quot;&quot;&quot;最后，建立一个回测Backtest，在策略与数据之间建立逻辑联系。&quot;&quot;&quot;</span></span><br><span class="line">test = bt.Backtest(s, data)</span><br><span class="line"><span class="comment"># 然后就可以运行回测了</span></span><br><span class="line">res = bt.run(test)</span><br><span class="line"><span class="comment"># 之后就可以查看结果了</span></span><br><span class="line">res.plot()</span><br><span class="line">res.display()</span><br></pre></td></tr></table></figure>
<p>以上是文档里的例子，还有其它例子，我照着敲了一遍，详见btExample.py。<br>总结一下，使用bt框架的主要步骤是:获取数据-&gt;建立策略-&gt;建立回测-&gt;运行回测-&gt;获得结果。关键是建立策略。<br>下面开始用bt跑我自己的数据跟策略吧。首先先获得数据，就用我之前下载到csv文件的数据吧。</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">#获取数据</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">GetData</span>():</span></span><br><span class="line">    <span class="comment">#读取数据</span></span><br><span class="line">    df_300 = pd.read_csv(<span class="string">&quot;df_300_hist.csv&quot;</span>)</span><br><span class="line">    df_nas = pd.read_csv(<span class="string">&quot;df_nas_hist.csv&quot;</span>)</span><br><span class="line">    <span class="comment">#只保留收盘价</span></span><br><span class="line">    length1 = <span class="built_in">len</span>(df_300)</span><br><span class="line">    length2 = <span class="built_in">len</span>(df_nas)</span><br><span class="line">    df_300 = df_300.loc[<span class="number">0</span>:length1, [<span class="string">&quot;date&quot;</span>, <span class="string">&quot;close&quot;</span>]]</span><br><span class="line">    df_nas = df_nas.loc[<span class="number">0</span>:length2, [<span class="string">&quot;date&quot;</span>, <span class="string">&quot;close&quot;</span>]]</span><br><span class="line">    <span class="comment"># 更改数据格式，使其符合bt要求</span></span><br><span class="line">    df_300.columns = [<span class="string">&quot;Date&quot;</span>, <span class="string">&quot;300etf&quot;</span>]</span><br><span class="line">    df_300[<span class="string">&quot;Date&quot;</span>] = pd.to_datetime(df_300[<span class="string">&quot;Date&quot;</span>])</span><br><span class="line">    df_300.set_index(<span class="string">&quot;Date&quot;</span>, inplace = <span class="literal">True</span>)</span><br><span class="line">   </span><br><span class="line">    df_nas.columns = [<span class="string">&quot;Date&quot;</span>, <span class="string">&quot;nasetf&quot;</span>]</span><br><span class="line">    df_nas[<span class="string">&quot;Date&quot;</span>] = pd.to_datetime(df_nas[<span class="string">&quot;Date&quot;</span>])</span><br><span class="line">    df_nas.set_index(<span class="string">&quot;Date&quot;</span>, inplace = <span class="literal">True</span>)</span><br><span class="line">   </span><br><span class="line">    <span class="comment"># 合并数据</span></span><br><span class="line">    data = pd.concat([df_300, df_nas], axis = <span class="number">1</span>, join = <span class="string">&quot;inner&quot;</span>)</span><br><span class="line">   </span><br><span class="line">    <span class="comment"># 返回数据</span></span><br><span class="line">    <span class="keyword">return</span> data</span><br></pre></td></tr></table></figure>
<p>bt框架对数据的要求是以”Date”为index，所以进行了相应的转换，最后把数据合并。<br>接着开始建立策略，先用没有止盈止损的策略。</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 建立策略</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">CreateStrategy</span>(<span class="params">name</span>):</span></span><br><span class="line">    strategy = bt.Strategy(name,</span><br><span class="line">    [bt.alogs.RunWeekly(),</span><br><span class="line">     bt.algos.SelectAll(),</span><br><span class="line">     bt.algos.WeighEqually(),</span><br><span class="line">     bt.algos.Rebalance()</span><br><span class="line">    ])</span><br><span class="line">    <span class="keyword">return</span> strategy</span><br></pre></td></tr></table></figure>
<p>主要参数就是策略名称，还有一个列表，包含的内容主要为执行策略的间隔，选股策略，权重，以及是否再平衡。一些常用的选项都在bt.algos模块里定义了，如果不满足需要，可以自行定义。<br>策略建立好以后就建立回测，所谓回测就是把策略和数据结合在一起的结构。在其中也可以指定投入初始资金等。注意策略会进行深度复制，即在回测中原始数据不会被改变，可以用在其它回测中。</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">test = bt.Backtest(strategy, data)</span><br></pre></td></tr></table></figure>
<p>最后运行回测</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">res = bt.run(test)</span><br></pre></td></tr></table></figure>
<p>以不同的方式输出回测结果</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 输出回测结果</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">outputResult</span>(<span class="params">res</span>):</span></span><br><span class="line">    fig = res.plot()</span><br><span class="line">    plt.savefig(<span class="string">&quot;BTStimulateTest.png&quot;</span>)</span><br><span class="line">    res.display()</span><br><span class="line">    fig = res.plot_histogram()</span><br><span class="line">    plt.savefig(<span class="string">&quot;BTStimulateHistTest.png&quot;</span>)</span><br><span class="line">    res.plot_security_weights()</span><br><span class="line">    plt.savefig(<span class="string">&quot;BTStimulateWeights.png&quot;</span>)</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0137-etfdata/01.png"><br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0137-etfdata/02.png"><br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0137-etfdata/03.png"><br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0137-etfdata/04.png"><br>搞定!总收益率110%，最大回撤26.5%。还不错。但是还有个问题，怎么设定添加交易手续费？<br>方法是定义一个计算手续费的函数，</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 计算交易手续费</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">getFee</span>(<span class="params">quantity, price</span>):</span></span><br><span class="line">    rate = <span class="number">0.0003</span></span><br><span class="line">    fee = quantity*price*rate</span><br><span class="line">    <span class="keyword">if</span> fee &lt; <span class="number">0.1</span>:</span><br><span class="line">        fee = <span class="number">0.1</span></span><br><span class="line">    <span class="keyword">return</span> fee</span><br></pre></td></tr></table></figure>
<p>然后用strategy.set_commissions(getFee)将该函数传递给策略。测试了一下(把佣金费率改大，到3%。再大，比如到30%，运行会报错。)，总收益率是下降的。说明是对的。<br>接下来就是怎么把止盈止损策略加到策略里的问题了。下次吧。<br>我发文章的三个地方，欢迎大家在朋友圈等地方分享，欢迎点“好看”。<br>我的个人博客地址：<a href="https://zwdnet.github.io/">https://zwdnet.github.io</a><br>我的个人博客地址：<a href="https://zwdnet.github.io/">https://zwdnet.github.io</a><br>我的CSDN博客地址：<a target="_blank" rel="noopener" href="https://blog.csdn.net/zwdnet">https://blog.csdn.net/zwdnet</a><br>我的微信个人订阅号：赵瑜敏的口腔医学学习园地</p>
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